{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "db523bc4-c949-4cc0-b400-b69eb4d645d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72b13e54-c044-4968-9b6f-32aa65ee1b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sigmoid 1/1+e的(-z)次   这里z取2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4874ed2-f6c6-40c7-929a-8eff9d46a4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    a = []\n",
    "    for item in x:\n",
    "        a.append(1.0/(1.0 + math.exp(-item))) # 将x经过sig函数处理后的y放入数组a\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "343afc2e-1729-45a9-897e-4fd4f66eecd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-10, 10, 0.1)\n",
    "y = sigmoid(x)\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "559a7b2f-5582-40cc-af98-eab5a2e3e8b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
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